| This paper is an introduction to nonparametric regression for research applications in the social sciences. Nonparametric regression is a regression technique, which does not require specification of an a priori functional form. The paper focuses on kernel regression, which is a particular nonparametric regression estimator, for predictive purposes. It is also suggested that the kernel estimator can be used as a test for unspecified nonlinearity in a parametric regression function. A Monte-Carlo study is used to study the performance of kernel regression in prediction and hypothesis testing. Results suggest that kernel regression yielded better out-of-sample prediction of moderated regression models than did a misspecified ordinary least squares regression equation. Results also suggest that a kernel F-test for lack of fit yielded approximately correct Type I error rates with large samples, and strong statistical power, for detecting the existence of an unspecified moderator variable in simulated regression data. |
